1. Field of the Invention
Generally, the present disclosure relates to the field of fabricating microstructures, such as integrated circuits, and, more particularly, to the throughput characteristics of complex process tools, such as cluster tools, used for the fabrication of semiconductor devices or other microstructures.
2. Description of the Related Art
Today's global market forces manufacturers of mass products to offer high quality products at a low price. It is thus important to improve yield and process efficiency to minimize production costs. This holds especially true in the field of microstructure fabrication, for instance for manufacturing semiconductor devices, since, in this field, it is essential to combine cutting-edge technology with volume production techniques. It is, therefore, the goal of manufacturers of semiconductors, or generally of microstructures, to reduce the consumption of raw materials and consumables while at the same time improve process tool utilization. The latter aspect is especially important since, in modern semiconductor facilities, equipment is required which is extremely cost-intensive and represents the dominant part of the total production costs. At the same time, the process tools of the semiconductor facility have to be replaced more frequently compared to most other technical fields due to the rapid development of new products and processes, which may also demand correspondingly adapted process tools.
Integrated circuits are typically manufactured in automated or semi-automated facilities, thereby passing through a large number of process and metrology steps to complete the device. The number and the type of process steps and metrology steps a semiconductor device has to go through depends on the specifics of the semiconductor device to be fabricated. A usual process flow for an integrated circuit may include a plurality of photolithography steps to image a circuit pattern for a specific device layer into a resist layer, which is subsequently patterned to form a resist mask for further processes in structuring the device layer under consideration by, for example, etch or implant processes and the like. Thus, layer after layer, a plurality of process steps are performed based on a specific lithographic mask set for the various layers of the specified device. For instance, a sophisticated CPU requires several hundred process steps, each of which has to be carried out within specified process margins so as to fulfill the specifications for the device under consideration. As the majority of the process margins are device-specific, many of the metrology processes and the actual manufacturing processes are specifically designed for the device under consideration and require specific parameter settings at the adequate metrology and process tools.
In a semiconductor facility, a plurality of different product types are usually manufactured at the same time, such as memory chips of different design and storage capacity, CPUs of different design and operating speed and the like, wherein the number of different product types may even reach a hundred and more in production lines for manufacturing ASICs (application specific ICs). Since each of the different product types may require a specific process flow, possibly based on different mask sets for the lithography, specific settings in the various process tools, such as deposition tools, etch tools, implantation tools, chemical mechanical polishing (CMP) tools and the like, may be necessary. Consequently, a plurality of different tool parameter settings and product types may be simultaneously encountered in a manufacturing environment.
Hereinafter, the parameter setting for a specific process in a specified process tool or metrology or inspection tool may commonly be referred to as process recipe or simply as recipe. Thus, a large number of different process recipes, even for the same type of process tools, may be required which have to be applied at the process tools at the time the corresponding product types are to be processed in the respective tools. However, the sequence of process recipes performed in process and metrology tools or in functionally combined equipment groups, as well as the recipes themselves, may have to be frequently altered due to fast product changes and the highly variable processes involved. As a consequence, tool performance, especially in terms of throughput, is a very critical manufacturing parameter as it significantly affects the overall production costs of the individual devices. The progression of throughput over time of individual process and metrology tools, or even certain entities thereof, such as process modules, substrate robot handlers, load ports and the like, may, however, remain unobserved due to the complexity of the manufacturing sequences including a large number of product types and a corresponding large number of processes, which in turn are subjected to frequent recipe changes.
Recently, process tools have become more complex in that a process tool may include a plurality of functional modules or entities, referred to as cluster or cluster tool, which may operate in a parallel and/or sequential manner such that products arriving at the cluster tool may be operated therein in a plurality of process paths or tool-internal process flows, depending on the process recipe and the current tool state. The cluster tool may enable the performance of a sequence of correlated processes, thereby enhancing overall efficiency by, for instance, reducing transport activities within the factory and/or increasing tool capacity and availability by using several process chambers in parallel for the same process step. However, due to the plurality of entities involved, the interdependence of process recipes, tool-internal process paths and other parameters, such as the delivery strategy to the tool by the global transport system, the configuration of the interface between the global transport system and tool, i.e., the number of load ports, and the like, is very difficult to assess, for instance, in view of the overall tool performance in terms of tool throughput.
As a consequence, the overall equipment performance, for instance in terms of yield and throughput, is a very critical and complex manufacturing parameter as it significantly affects the overall production costs of the individual devices. The progression of tool yield and throughput over time of individual process and metrology tools or even certain entities thereof, such as process modules, substrate robot handlers, load ports and the like, may remain unobserved due to the complexity of the manufacturing sequences including a large number of product types and a corresponding large number of processes, which in turn are subjected to frequent recipe changes. Hence, low-performing tools may remain undetected for a long time when the performance of an equipment group which the tool under consideration belongs to is within its usual performance margin that typically has to be selected to allow a relatively wide span of variations, owing to the complexity of the processes and the tools involved.
For this reason, it is of great importance for the semiconductor manufacturer to monitor and determine corresponding metrics that provide a measure for the performance of individual process tools, thereby also enabling tool suppliers to specifically improve software and hardware components of process tools on the basis of the data provided by the manufacturers. Since tool requirements may significantly depend on manufacturer-specific conditions, a plurality of industrial standards have been defined to provide a foundation for defining a common global set of semiconductor equipment requirements, thereby reducing company-specific requirements for production equipment while, on the supplier side, attention may be focused on improving process capabilities instead of maintaining many customer-specific products. Thus, in some industrial fields, a plurality of equipment-specific standards have been defined relating to the definition of equipment messages, which for the semiconductor industry are known under SECS (SEMI (Semiconductor Equipment and Materials Institute) Equipment Communications Standard), which establish a common language for a communication between process tools and a remote host system. Similarly, a plurality of standards are established for defining the tool performance. For example, in the field of semiconductors, the E10 and E58 standards provide a basis to assess the reliability, availability and the maintainability (RAM) of process tools using standard tool states. Other standards, such as the E116 standard, have been introduced to describe the performance of process tools based on a state model, wherein the tool state is automatically reported by providing state transitions and run rate information.
Consequently, in a complex manufacturing environment, a large amount of information related to the process tool may be available with a varying degree of resolution, depending on the overall configuration of a control system of the manufacturing environment under consideration. That is, the respective state models for representing the individual process tools in the manufacturing environment may allow monitoring and controlling of the process tools at a higher abstract level, for instance by a supervising MES (Manufacturing Execution System) while, at a lower level, even information with respect to the operational state of individual entities of the various process tools may be available in the form of respective tool messages. In this respect, a functional entity of a process tool is to be understood as any unit that may hold a work piece, such as a substrate for forming semiconductor devices such as a process chamber, a robot handler and the like. Hence, although a large amount of valuable tool-specific information may be available in the manufacturing environment, an estimation and monitoring or controlling of throughput-related aspects of the process tools may be difficult, since usually process lines may suffer from unobserved throughput losses caused by, for instance, process and set-up changes and/or equipment malfunctions and the like. That is, due to the complexity of modern semiconductor facilities, a certain degree of variability of the manufacturing process average throughput rates calculated from lot processing times or from state output data may not efficiently allow detection of tool performance of individual tools or tool groups with respect to throughput. For example, in many process tools, a plurality of functional entities may be process chambers for performing a respective process on the basis of a dedicated process recipe. In many process tools, two or more equivalent process chambers may be provided so as to allow a certain degree of parallelism when processing the substrates according to the specified recipe. Similarly, a plurality of different process steps may be integrated into a single tool, which may also be referred to as cluster tool, so that a plurality of process steps may be performed on the basis of different process recipes, while other functional entities may be responsible for providing the required substrate handling resources, such as load ports, robot handlers and the like. Thus, the throughput of a complex process tool may significantly depend on the basic configuration in terms of substrate handling resources and actual process models, in combination with the specific process recipes to be applied in the various process modules.
For example, upon reduced functionality or even complete failure of one of a plurality of parallel or equivalent process modules, the process tool may still be operable, but may have a reduced throughput, which may possibly remain undetected on the basis of determining throughput of equipment groups, for instance, on a monthly basis, as is frequently practiced in semiconductor facilities. However, even if tool messages may be monitored to detect the current operational status of the process tool under consideration, an estimation of the throughput may be obtained on the basis of number of lots or substrates up to a specific point in time and the corresponding time interval required for processing the substrates or lots. Thus, only a “retrospective” estimation of the throughput may be obtained, while the “current” throughput may be difficult to estimate, in particular when the operational status of the process tool under consideration may have changed. That is, throughput may be dependent on the current status of the process tool and the respective process recipe to be applied in the various functional entities, so that conventional approaches relying on the number of substrates processed over the past time interval and the length of the time interval may not allow a predictive estimation of the throughput nor may these approaches enable an estimation of the throughput behavior for one or more process tools in view of different operational scenarios so as to estimate the throughput upon certain events, such as a variation of process recipes, reconfiguring the operational status of the process tool and the like.
For this purpose, throughput models have been proposed in order to provide a certain degree of “look aside” functionality with respect to the throughput of complex cluster tools. A throughput model is to be understood as a mathematical method or model to determine the throughput of specific process tools when operating on the basis of specified process recipes while also taking into consideration a number of conditions or parameters. The throughput is represented by the number of processed work pieces divided by the time interval necessary for processing these work pieces. In these models, it is further assumed that, before and after this time interval, the process tool is empty, that is, the respective entities of the process tools, such as robot handlers, process chambers and the like, do not contain any work pieces. The amount of work pieces processed within the time interval under consideration is also frequently called a cascade, wherein the individual elements of a cascade may represent individual work pieces, such as substrates for semiconductor devices, or groups of substrates, when the processing of the work pieces in the functional entities of the process tool may be performed on a group of substrates at a time. In this case, respective process tools may also be referred to as batch tools.
At the beginning of a cascade, i.e., when supplying the first work piece to the process tool, a certain time elapses before the first work piece is completely processed. This time, i.e., the time required for the first work piece to pass through all process steps to be performed for the specified work piece, may be referred to as first piece time, and this time interval is typically longer than the respective time intervals at which subsequent work pieces will leave the last process step in the process tool. Thus, in a respective throughput model, throughput may be determined by the length of a cascade divided by the entire process time, which may include the first piece time, i.e., the process time for the very first work piece of the cascade, and the intervals of the remaining work pieces of the cascade. Thus, in a throughput model, the conditions and parameters defining the first piece time and the time intervals of the subsequent pieces may have to be distinguished and quantified. This may be accomplished by two different approaches. For the first piece time, the actual process flow in the process tool under consideration may be determined, i.e., the actual sequence of processes to be performed in the individual process chambers on the basis of respective process recipes. On the other hand, for the time intervals of the subsequent work pieces, the tool resources are at the focus of interest. In this case, it may typically be necessary to consider all resources in order to determine a limiting or bottle-neck resource of the process tool, which may determine the time intervals for the subsequent work pieces.
Thus, determining predictive throughput models may require a certain degree of intuition and a high level of experience of the process engineer by extracting respective very limited information from a restricted amount of tool messages and by appropriately defining the material flows in the process tool under consideration. Hence, the conventional approach for generating a throughput model may be a time-consuming task, the result of which may depend on highly subjective criteria, which may result in inconsistencies when applying throughput models that are established by different process engineers. Additionally, the necessary time consumption for the model generation leads to long time intervals between updates to the throughput model, thereby leading to the usage of potentially outdated models.
The present disclosure is directed to various methods and systems that may avoid, or at least reduce, the effects of one or more of the problems identified above.